Analysis of Uplink Cell-Free Massive MIMO System With Mixed-ADC/DAC Receiver

Cell-free massive Multi-input Multi-output (MIMO) can offer higher spectral efficiency compared with cellular massive MIMO by providing services to users through the collaboration of distributed APs, and cell-free massive MIMO systems with distributed operations are attracting a great deal of industry attention due to their simplicity and ease of deployment. This paper aims to find an optimal solution for energy efficiency in the downlink operation in the Industrial Internet based on cell-free massive MIMO systems with distributed operations. A system model is proposed, and a theoretical analysis on energy efficiency is presented. The optimization problem of efficient downlink operation is formulated as a mixed-integer nonlinear programming (MINLP) problem, which is further decomposed into two sub-problems, i.e., maximizing the sum-rate of the downlink transmission and optimizing the total energy consumption. The two sub-problems are addressed via AP selection and power allocation, respectively. The simulation results demonstrate that our algorithms can significantly improve the energy efficiency with low computational complexity in comparison with traditional distributed cell-free massive MIMO. Even in the presence of pilot contamination, the proposed algorithms can still provide significant energy efficiency when a large number of IoTDs are connected.

In this paper, the spectral efficiency (SE) of an uplink hardware-constrained cell-free massive multi-input multi-output (MIMO) system with maximal ratio combining (MRC) receiver filters in the context of superimposed pilots (SPs) is investigated. Tractable closed-form SE expressions for the considered system are derived, which share us with opportunities to explore the impacts of the hardware quality coefficient, the length of coherence interval, and the power balance factor between pilot and data signals. Numerical results indicate that the achievable SE deteriorates as the hardware quality decreases and is more susceptible to the hardware impairments at the user equipments (UEs). Besides, we observe that SPs outperform regular pilots (RPs) in terms of SE and this performance gain is heavily dependent on the values of power balance factor and coherence interval. However, the superiorities of SPs over RPs have vanished when severe hardware imperfections are considered.

This paper investigates rate-splitting multiple access (RSMA) in a cell-free massive multi-input multi-output (MIMO) system with integrated sensing and communication (ISAC). In this sytem model, we consider the maximization of the minimum rate subject to radar sensing constraints and a maximum power budget. A successive convex approximation based scheme is developed to solve the non-convex problem by optimizing the precoding vectors for radar and communication, as well as the common rate variables. Numerical results show the superior performance of the proposed RSMA method over conventional cell-free massive MIMO schemes under different scenarios with varying numbers of APs, antennas, and users.

The evolving 5G standards promise green communications with enhanced data services and significant link reliability. Massive multi input multi output (MIMO) techniques are the driving force behind green communications, since they provide better energy efficiency with reduced transmit power. The massive data generated from such mobile communication systems, is a rich data source of great value. Procuring useful analytics from this precious resource, a big data aware 5G mobile communication system can be developed. A particular choice of big analytics brings in the concept of large random matrix models and single ring law. In this paper, first, big data analytics is performed in the context of a mobile user communicating to, either a massive MIMO or a massive MIMO orthogonal frequency division multiplexing (OFDM) system. Constructive insights such as transmitted (source) signal correlation analysis (attributed to certain network events), channel correlation analysis (attributed to user mobility) have been extracted. Ring law also has its roots in signal detection, which suggests that few other signal detection algorithms may be suitable candidates for signal/channel correlation analysis. Therefore, second, a proposed extension of an information theoretic criterion (ITC) based signal detection algorithm, for correlation analysis, is compared with ring law. Using massive MIMO and MIMO-OFDM system simulations, the said correlation analyses have confirmed the prevalence of ring law. Third, it is deduced that integrating big data analytics with massive MIMO system improves spectral efficiency.

We consider the pilot assignment problem in cell-free massive multi-input multi-output (MIMO) networks, where a large number of remote radio head (RRH) antennas are randomly distributed in a wide area, and jointly serve a relatively smaller number of users (UE) coherently. By artificially imposing topological structures on the UE-RRH connectivity, we model the network by a partially-connected interference network and formulate the topological pilot assignment (TPA) problem as a sequential maximum weight induced matching problem that can be solved by either a mixed integer linear program or a simple yet efficient greedy algorithm. The efficiency of the proposed algorithms is evaluated in cell-free massive MIMO networks.

This paper investigates the achievable uplink spectral efficiency (SE) of multi-cell massive multi-input multioutput (MIMO) systems with the low-resolution analog-to-digital converter (ADC) receiver architecture. For the maximum ratio combining detector, we derive an approximate expression for the achievable SE of the system, which enables the characterization of the impact of key system parameters such as transmit power, the number of antennas, and the ADC resolution bits on the achievable SE. Moreover, simple power scaling laws are presented.

The topic of anonymous unmanned aerial vehicle (UAV) localizing based on angle estimation has been frequently discussed in the past few years. However, the existing methodologies are inefficient in a massive sensor arrays scenario. To avoid such drawback, a cooperative three-dimensional (3D) positioning methodology is introduced. The critical idea of the proposed localizing method is to estimate the two-dimensional angle of the anonymous UAV via a polarized massive multi-input multi-output (MIMO) system. To reduce the computational burden and explore the nature of the multi-dimensional data, a tensor compressive sampling (TCS) framework is proposed. Moreover, a closed-form estimation strategy is developed for 2D direction finding. Our framework is shown to be more efficient than the existing algorithm in terms of hardware/software complexity. Besides, it is suitable for a polarized MIMO system with an arbitrary array geometry. Several simulation examples are provided to show its improvement of the new methodology.

We consider antenna selection in massive multi-input multi-output (MIMO) systems. Most prior works ideally assume perfect channel estimation at the receiver side. In this paper, we present an antenna selection scheme based on a genetic algorithm for massive MIMO systems in the presence of channel estimation error (CEE). Numerical results suggest that the proposed algorithm outperforms traditional antenna selection algorithms in terms of channel capacity, especially for low signal-to-noise ratio (SNR) scenarios.

One bottleneck of the massive multi-input multi-output (MIMO) systems is the pilot contamination caused by users sharing the non-orthogonal pilots. The pilot contamination contributes significantly to the channel estimation error at the base station (BS) and finally results in the saturation of user rates. This paper firstly analyzes the mean square error (MSE) of the channel estimation at the BS. Inspired by the results, this paper then proposes a user pilot scheduling algorithm which preferentially minimizes the channel estimation MSE of users with poorer channel qualities. Simulation results show that the algorithm significantly improves the achievable rates of the poorer users as well as the system sum rate compared with the conventional way.

In this paper, we compare I/Q imbalance compensation schemes for massive multi-input multi-output (MIMO) system in presence of I/Q imbalance at both transmitter and receiver. Transceiver I/Q imbalance can be compensated jointly by the base station or separately by both base station and user terminals. We show that the achievable sum rate of self-compensation scheme which compensates its I/Q imbalance separately is severely deteriorated in presence of channel estimation error and the I/Q imbalance parameter estimation error. In contrast, the achievable sum rate of the joint compensation scheme with RZF precoding in the base station shows robust performance against channel estimation error and the I/Q imbalance parameter estimation error.

The orthogonal matching pursuit (OMP) algorithm is widely used because of its simple algorithmic structure, the linear magnitude of its computational complexity and other advantages. Algorithms of improved and extended OMP types are continually emerging. To make the OMP algorithm more suitable for the new transmission requirements of the fifth-generation (5G) network, the orthogonal matching pursuit with multiple users (OMP_mu) algorithm is proposed for cell-free massive multi-input multi-output (MIMO) networks with multiuser spatial index modulation. The OMP_mu algorithm considers the constraint condition for the sparsity of each user on the basis of each iterative search in the OMP algorithm until all users meet the constraint. The simulation results show that compared with the original OMP algorithm, the OMP_mu algorithm has a greatly improved demodulation performance. Compared with the orthogonal matching pursuit step-by-step (OMP_step) algorithm, which takes the user sparsity constraint condition into account in two steps, the OMP_mu algorithm greatly reduces the operation time and number of iterations while achieving a slightly better performance. The OMP_mu algorithm provides an effective reference for the application of spatial index modulation in cell-free massive MIMO networks.

The practical application for massive MIMO relaying system with a large number of antennas is considered a challenging problem. To solve this problem, a promising solution is to utilize double precision analog-to-digital converter (ADC) and digital-to-analog converter (DAC) at the relay. Depending on the additive quantization noise model, closed-form expression for system sum rate is analyzed. Furthermore, some asymptotic simplifications are utilized to reveal the effects of some parameters on the system performance. These parameters such as the proportion of full precision in the double precision architecture (κ), the number of quantization bits (b) and the number of relay antennas (N). In addition, a tradeoff between system sum rate and power consuming is discussed by deriving energy efficiency (EE). Moreover, the tradeoff between EE and the system sum rate is investigated with different numbers of full precision ADCs/DACs. Simulation results demonstrated that large antenna array with double precision ADCs/DACs can reduce hardware cost and power consuming for practical massive MIMO relaying systems.

Generally, the downlink channel state information (CSI) used for precoding is conveyed back in massive multi-input multi-output (MIMO) systems with frequency division duplex (FDD) mode, which costs too much bandwidth. However, most of CSI compression algorithms including deep learning based schemes show poor performance with high compression ratio (CR) in ourdoor scenery. In this letter, we propose a new neural network that utilizes the deep neural network (DNN) to improve the learning ability of residual network and a novel activation function to adapt the neural network. Simulation results show that the proposed CsiNet+DNN method exhibits better performance than exsiting methods in recovery quality and accuracy, which also achieves remarkable robustness at noise situations.

We propose a new hybrid precoding technique for massive multi-input multi-output (MIMO) systems using spatial channel covariance matrices in the analog precoder design. Applying a regularized zero-forcing precoder for the baseband precoding matrix, we find an unconstrained analog precoder that maximizes signal-to-leakage-plus-noise ratio (SLNR) while ignoring analog phase shifter constraints. Subsequently, we develop a technique to design a constrained analog precoder that mimics the obtained unconstrained analog precoder under phase shifter constraints. The main idea is to adopt an additional baseband precoding matrix, which we call a compensation matrix. We analyze the SLNR loss due to the proposed hybrid precoding compared to fully digital precoding, and determine which factors have a significant impact on this loss. In the simulations, we show that if the channel is spatially correlated and the number of users is smaller than the number of RF chains, the SLNR loss becomes negligible compared to fully digital precoding. The main benefit of our method stems from the use of spatial channel matrices in such a way that not only is each user's desired signal considered, but also the inter-user interference is incorporated in the analog precoder design.

This paper investigates the massive multi-input multi-output (MIMO) system in practical deployment scenarios, in which, to balance the economic and energy efficiency with the system performance, the number of radio frequency (RF) chains is smaller than the number of antennas. The base station employs antenna selection (AS) to fully harness the spatial multiplexing gain. Conventional AS techniques require full channel state information (CSI), which is time-consuming as the antennas cannot be simultaneously connected to the RF chains during the channel estimation process. To tackle this issue, we propose a novel joint channel prediction and AS (JCPAS) framework to reduce the CSI acquisition time and improve the system performance under temporally correlated channels. Our proposed JCPAS framework is a fully probabilistic model driven by deep unsupervised learning. The proposed framework is able to predict the current full CSI, while requiring only a historical window of partial observations. Extensive simulation results show that the proposed JCPAS can significantly improve the system performance under temporally correlated channels, especially for very large-scale systems with highly correlated channels. multi-output (MIMO) system in practical deployment scenarios, in which, to balance the economic and energy efficiency with the system performance, the number of radio frequency (RF) chains is smaller than the number of antennas. The base station employs antenna selection (AS) to fully harness the spatial multiplexing gain. Conventional AS techniques require full channel state information (CSI), which is time-consuming as the antennas cannot be simultaneously connected to the RF chains during the channel estimation process. To tackle this issue, we propose a novel joint channel prediction and AS (JCPAS) framework to reduce the CSI acquisition time and improve the system performance under temporally correlated channels. Our proposed JCPAS framework is a fully probabilistic model driven by deep unsupervised learning. The proposed framework is able to predict the current full CSI, while requiring only a historical window of partial observations. Extensive simulation results show that the proposed JCPAS can significantly improve the system performance under temporally correlated channels, especially for very large-scale systems with highly correlated channels.